The overall goal of this project is to test two strategies for implementing a shared decision making tool to be used by providers while talking to patients about lung cancer screening. Eight participating sites will be randomized to compare standard implementation with intensive implementation. Additionally, the investigators will determine the factors that were most important for successful implementation of the shared decision making tool. Finally, the investigators will survey patients to evaluate the effects of Decision Precision on patient's knowledge of the risks and benefits of lung cancer screening, the quality of their decision making, and their satisfaction with care.
The overall goal of this quality improvement project is to test two strategies for implementing shared decision making, which incorporates the Decision Precision lung cancer screening tool. The investigators will use multi-site, cluster-based randomization to compare standard implementation with intensive implementation. Additionally, the investigators will determine the factors that were most important for successful implementation of the shared decision making tool. Finally, the investigators will have a human subjects research component to evaluate the effects of Decision Precision on patient's knowledge of the risks and benefits of lung cancer screening, the quality of their decision making, and their satisfaction with care.
Study Type
OBSERVATIONAL
Enrollment
17,033
All sites will receive a professionally developed, 15-minute webinar that describes the tool's development (e.g., how the algorithm was designed) and a tutorial on how to use the web-site. The investigators will promote the webinar and using the tool through key local leaders. A web-link to use the tool will be placed within the lung cancer screening clinical reminder.
Sites randomized to intensive implementation will be offered participation in the LEAP program. LEAP (Learn. Engage. Act. Program) is a multi-week, online learning collaborative using systems redesign techniques to help sites identify and overcome barriers to implementation of shared decision making (use of the Decision Precision tool).
VA Ann Arbor Healthcare System, Ann Arbor, MI
Ann Arbor, Michigan, United States
Minneapolis VA Health Care System, Minneapolis, MN
Minneapolis, Minnesota, United States
Manhattan Campus of the VA NY Harbor Healthcare System, New York, NY
New York, New York, United States
Odds Ratio of the Interaction Between Lung Cancer Risk and Implementation Arm
First, we estimated screening's net benefit for an individual based on their baseline lung cancer risk, as estimated using the Bach et. al. annual lung cancer incidence model. Patients are considered "high benefit" if their annual lung cancer risk is between 0.3%-1.3%. Patients outside this range are considered "preference sensitive". We fit a multilevel logistic regression model where receipt of screening is the outcome. Precision decision making is reflected in the association between baseline lung cancer risk and screening utilization: an increase in screening utilization for those at higher lung cancer risk indicates some degree of precision decision making. The primary outcome for the cluster-randomized design assesses the difference in precision decision making in the standard vs. intensive implementation arms. This is estimated as the effect on screening receipt of the interaction between risk and implementation arm.
Time frame: post implementation, an average of 15 months
Patient Satisfaction With Decision and Process
Obtained from patient surveys (for the subset of the overall participants who returned surveys). The unit of measurement is one unit on the scale \[scale of 0 (very poor) to 10 (very good)\].
Time frame: Survey mailed to Veteran several weeks after identified as having an initial discussion about lung cancer screening using VA administrative data
Number of Tool Assessments Where Patient Decision Aid Was Printed
Number of times during study duration where patient decision aid was printed from the Lung Decision Precision web-site.
Time frame: post implementation, up to 25 months
Number of Times Dynamic Pictograph Depicting Personalized Benefit and Harm Was Opened for Display
Number of times dynamic pictograph depicting personalized benefit and harm was opened for display in the Lung Decision Precision web-site during the study period, collected as para data from Decision Precision web-site
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Providers at all sites will have access to this system. It will provide feedback on the screening and shared decision making process (e.g., number of provider's eligible patients screened for lung cancer, use of the tool, patient knowledge and satisfaction from patient surveys).
All sites will be offered an academic detailing approach to implementation of Lung Decision Precision. A trained detailer will travel to all sites who agree to a visit. The detailer will meet with individual primary care providers whenever possible, and groups of providers as necessary. He will present evidence for a risk-based approach to screening and how to use the tool with patients to make tailored screening recommendations.
Durham VA Medical Center, Durham, NC
Durham, North Carolina, United States
Cincinnati VA Medical Center, Cincinnati, OH
Cincinnati, Ohio, United States
VA Portland Health Care System, Portland, OR
Portland, Oregon, United States
Providence VA Medical Center, Providence, RI
Providence, Rhode Island, United States
Ralph H. Johnson VA Medical Center, Charleston, SC
Charleston, South Carolina, United States
Time frame: post implementation, up to 25 months
Formative Evaluation to Determine the Factors Most Important for Successful Implementation of Decision Precision Tool
Qualitative analysts will conduct telephone interviews with providers at each site who identify themselves as participating in shared decision making process with patients regarding lung cancer screening. Reporting for this report is number of interviews completed of number interviews requested.
Time frame: At least one year post-implementation of Lung Decision Precision web-site